CN117137639B - Signal filtering method, device, computer equipment and storage medium - Google Patents
Signal filtering method, device, computer equipment and storage medium Download PDFInfo
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- 238000001914 filtration Methods 0.000 title claims abstract description 107
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 31
- 238000011282 treatment Methods 0.000 claims abstract description 14
- 238000011221 initial treatment Methods 0.000 claims abstract description 13
- 238000002347 injection Methods 0.000 claims abstract description 7
- 239000007924 injection Substances 0.000 claims abstract description 7
- 239000013598 vector Substances 0.000 claims description 41
- 239000011159 matrix material Substances 0.000 claims description 16
- 230000006870 function Effects 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 9
- 230000004044 response Effects 0.000 claims description 5
- 230000000694 effects Effects 0.000 abstract description 7
- 230000006378 damage Effects 0.000 abstract description 5
- 210000001525 retina Anatomy 0.000 abstract description 5
- 208000027418 Wounds and injury Diseases 0.000 abstract description 3
- 208000014674 injury Diseases 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000002324 minimally invasive surgery Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/30—Surgical robots
- A61B34/37—Master-slave robots
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1101—Detecting tremor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
- A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
- A61F9/00—Methods or devices for treatment of the eyes; Devices for putting-in contact lenses; Devices to correct squinting; Apparatus to guide the blind; Protective devices for the eyes, carried on the body or in the hand
- A61F9/007—Methods or devices for eye surgery
- A61F9/00736—Instruments for removal of intra-ocular material or intra-ocular injection, e.g. cataract instruments
Abstract
The invention discloses a signal filtering method which is applied to subretinal injection operation, and comprises the following steps: acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise; performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm; carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter; and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter. The method and the device simultaneously eliminate the influence of Gaussian noise and human hand physiological jitter on the motion of the slave robot through combining a Kalman filtering algorithm, a digital filter and a band-limited Fourier linear combination filter. The fitting effect is improved, and the occurrence of retina injury and potential safety hazards of the robot are avoided.
Description
Technical Field
The present invention relates to the field of subretinal injection surgery, and in particular, to a signal filtering method, apparatus, computer device, and storage medium.
Background
The subretinal injection operation has the advantages of narrow operation space and fine operation scale, and the master-slave robot system is adopted to assist the operation, so that the operation precision of doctors can be effectively improved, and the influence of hand shake on the operation effect is reduced. The master-slave robot collects the motion trail of the hands of the doctor through the master manipulator, and maps the trail to the slave manipulator after the motion is scaled, so that master-slave motion is realized. The signals collected by the main operator comprise human hand physiological shaking signals and Gaussian noise signals of the main operator besides human hand motion tracks.
The prior art often employs Band-limited fourier based linear combination filters (Band-limited Multiple Fourier Linear Combiner, BMFLC) to filter human hand physiological dither signals. However, this technique has two problems: firstly, the technology focuses on the filtering of the physiological shake signals of the human hand, but besides the physiological shake signals of the human hand, gaussian noise of the main operator can cause high-frequency vibration of the auxiliary operator so as to influence the motion stability of the auxiliary operator. Then the filter based on the band-limited fourier linear combination has poor fitting effect when processing the abrupt signal. In practical application, the mutation signal caused by misoperation of doctors can cause the robot to damage retina, so that potential safety hazard occurs.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a signal filtering method, apparatus, computer device, and storage medium for solving the above-mentioned problems.
A method of filtering a signal for use in subretinal injection surgery, the method comprising:
acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
In one embodiment, filtering gaussian noise in the operation signal includes:
and carrying out primary filtering treatment on the Gaussian noise by a Kalman filtering algorithm, and carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment by a digital filter.
In one embodiment, the first order filtering of the gaussian noise by a kalman filter algorithm includes: respectively establishing a motion model and an observation model;
the motion model:
（1）
the observation model:
（2）
the motion model is uniform motion, wherein,，/>the position and the speed of the moment k in the x, y and z directions; a is a transfer matrix, Q is a process noise variance matrix, H is an observation matrix, and R is an observation noise variance matrix; the expression is as follows:
（3）
（4）
（5）
（6）
wherein,
（7）
（8）
（9）
for the acquisition time interval, +.>、/>、/>For each direction continuous time variance +.>For each observed noise variance.
In one embodiment, the performing the second-stage filtering on the gaussian noise after the first-stage processing through the digital filter includes:
the expression of the digital filter is as follows:
（10）
wherein,is the frequency response of the digital filter.
In one embodiment, the filtering the physiological shake signal of the human hand in the operation signal includes:
determining an initialization coefficient vector, wherein the initialization coefficient vector is a zero vector with a size;
determining a window function, the window function beingLVector of size;
determining sine and cosine fitting values at the current moment;
determining an initial fitting error according to the initialization coefficient vector, the sine and cosine fitting value and an operation signal after Gaussian noise is filtered;
determining an initial in-band fitting signal according to the initialization coefficient vector, the window function and the sine and cosine fitting value;
determining an initial human hand movement track signal according to the fit signal in the frequency band and the operation signal after Gaussian noise is filtered;
and updating the initialization coefficient vector according to the initialization coefficient vector, the initial fitting error and the sine and cosine fitting numerical value.
In one embodiment, the determining the initial fitting error according to the initialization coefficient vector, the sine and cosine fitting value and the operation signal after filtering the gaussian noise includes:
（11）
wherein,error of initial fitting +.>To>Filtering the operation signal +.>Fitting a numerical value for sine and cosine of the current moment, +.>To initialize the coefficient vector.
In one embodiment, the determining the initial human hand movement trace signal according to the in-band fitting signal and the operation signal after filtering the gaussian noise includes:
（12）
wherein,for the human hand movement track signal, +.>To>The operation signal after the filtering is carried out,the signal is fit for an initial in-band.
In one embodiment, the updating the initialization coefficient vector according to the initialization coefficient vector, an initial fitting error, and a sine and cosine fitting value includes:
（13）
wherein,for the updated initialization coefficient vector +.>For initializing coefficient vectors, ++>Convergence rate coefficient when being the minimum mean square error algorithm, < ->Error of initial fitting +.>And fitting a numerical value for the sine and cosine of the current moment.
A signal filtering apparatus, the apparatus comprising:
the acquisition module is used for acquiring operation signals, and the operation signals comprise: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
the first filtering module is used for carrying out primary filtering processing on the Gaussian noise through a Kalman filtering algorithm;
the second filtering module is used for carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through the digital filter;
and the third filtering module is used for filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
The method and the device simultaneously eliminate the influence of Gaussian noise and human hand physiological jitter on the motion of the slave robot through combining a Kalman filtering algorithm, a digital filter and a band-limited Fourier linear combination filter. The fitting effect is improved, and the occurrence of retina injury and potential safety hazards of the robot are avoided.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Wherein:
FIG. 1 is a flow chart of a method of signal filtering in one embodiment;
FIG. 2 is a flow chart of a signal filtering method in another embodiment;
FIG. 3 is a block diagram of a signal filtering device in one embodiment;
FIG. 4 is a block diagram of a computer device in one embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The subretinal injection operation has the characteristics of narrow operation space, fine operation scale, high positioning precision, high stability and repeatability, and the surgical robot provides a solution for performing efficient and safe minimally invasive surgery. According to the operation mode, the existing minimally invasive surgical robots can be mainly classified into: collaborative robots, master-slave robots, and handheld robots. The master-slave robot has the advantages that direct physical connection between master hands and slave hands is not needed, possibility is provided for remote operation, operators can be arranged at positions which are more in accordance with human engineering, and the working strength of doctors is effectively reduced, so that the master-slave robot is widely applied in the medical field. The master-slave robot collects the motion trail of the hands of the doctor through the master manipulator, and maps the trail to the slave manipulator after the motion is scaled, so that master-slave motion is realized. The signals collected by the main operator comprise human hand physiological shaking signals and Gaussian noise signals of the main operator besides human hand motion tracks. The prior art often employs Band-limited fourier based linear combination filters (Band-limited Multiple Fourier Linear Combiner, BMFLC) to filter human hand physiological dither signals. However, this technique has two problems: firstly, the technology focuses on the filtering of the physiological shake signals of the human hand, but besides the physiological shake signals of the human hand, gaussian noise of the main operator can cause high-frequency vibration of the auxiliary operator so as to influence the motion stability of the auxiliary operator. Then the filter based on the band-limited fourier linear combination has poor fitting effect when processing the abrupt signal. In practical application, the mutation signal caused by misoperation of doctors can cause the robot to damage retina, so that potential safety hazard occurs. In order to solve the above technical problem, the present application provides a signal filtering method, in subretinal injection operation, as shown in fig. 1, the method includes:
s100: acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
s200: performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
s300: carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
s400: and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
In one embodiment, the first order filtering of the gaussian noise by a kalman filter algorithm includes: respectively establishing a motion model and an observation model;
the motion model:
（1）
the observation model:
（2）
wherein,，/>the position and the speed of the moment k in the x, y and z directions; a is a transfer matrix, Q is a process noise variance matrix, H is an observation matrix, and R is an observation noise variance matrix; the expression is as follows:
（3）
（4）
（5）
（6）
wherein,
（7）
（8）
（9）
for the acquisition time interval, +.>、/>、/>For each direction continuous time variance +.>For each observed noise variance.
In one embodiment, the data processed by the Kalman filtering algorithm is essentially a fusion of the data of the calculation result and the observation result, and the processed data still contains a small Gaussian noise. Thus, a digital filter may be used to apply smaller gaussian noise, and thus, the second filtering of the gaussian noise after the first filtering by the digital filter includes:
the expression of the digital filter is as follows:
（10）
wherein,is the frequency response of the digital filter.
In one embodiment, since the primary operator is operated, a sudden change signal is inevitably generated, and the BMFLC algorithm generates a long-term oscillation when processing the sudden change signal. Windowed Fourier linear combination (BMFLC) algorithm multiplies the sine and cosine components of the corresponding frequencies in the BMFLC algorithm by a window function with respect to frequencywTo change its approximate frequency response. The approximate frequency response of the algorithm after improvement is defined by a window functionwCan be selected according to the actual situationwA function. The improved algorithm is equivalent to adding transition bands on two sides of an ideal band-pass filter approximated by the original algorithm to eliminate the Gibbs effect.
As shown in fig. 2, the filtering the physiological shake signal of the human hand in the operation signal includes:
s401: determining an initialization coefficient vector, wherein the initialization coefficient vector is a zero vector with a size;
s402: a window function is determined and a window function is determined,the window function isLVector of size; wherein,
（11）
Lfor the passband to be equally divided by a number,to fit the minimum frequency of the frequency band>For-the maximum frequency of the fit frequency band,Gdividing the number for each Hz frequency;
s403: determining sine and cosine fitting values at the current moment;
s404: determining an initial fitting error according to the initialization coefficient vector, the sine and cosine fitting value and an operation signal after Gaussian noise is filtered;
s405: determining an initial in-band fitting signal according to the initialization coefficient vector, the window function and the sine and cosine fitting value;
s406: determining an initial human hand movement track signal according to the fit signal in the frequency band and the operation signal after Gaussian noise is filtered;
s407: and updating the initialization coefficient vector according to the initialization coefficient vector, the initial fitting error and the sine and cosine fitting numerical value.
In one embodiment, the determining the initial fitting error according to the initialization coefficient vector, the sine and cosine fitting value and the operation signal after filtering the gaussian noise includes:
（12）
wherein,error of initial fitting +.>To>Filtering the operation signal +.>Fitting a numerical value for sine and cosine of the current moment, +.>To initialize the coefficient vector.
In one embodiment, the determining the initial human hand movement trace signal according to the in-band fitting signal and the operation signal after filtering the gaussian noise includes:
（13）
wherein,for the human hand movement track signal, +.>To>The operation signal after the filtering is carried out,the signal is fit for an initial in-band.
In one embodiment, the updating the initialization coefficient vector according to the initialization coefficient vector, an initial fitting error, and a sine and cosine fitting value includes:
（14）
wherein,to updated initialization coefficientQuantity (S)>For initializing coefficient vectors, ++>Convergence rate coefficient when being the minimum mean square error algorithm, < ->Error of initial fitting +.>And fitting a numerical value for the sine and cosine of the current moment.
The present application further provides a signal filtering apparatus, as shown in fig. 3, including:
an acquisition module 10, configured to acquire an operation signal, where the operation signal includes: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
a first filtering module 20, configured to perform a first-stage filtering process on the gaussian noise by using a kalman filtering algorithm;
a second filtering module 30, configured to perform a second filtering process on the gaussian noise subjected to the first processing by using a digital filter;
and a third filtering module 40, configured to filter out the physiological shake signal of the human hand in the operation signal through a band-limited fourier linear combination filter.
The present application also provides a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
s100: acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
s200: performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
s300: carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
s400: and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
The present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
s100: acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
s200: performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
s300: carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
s400: and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
The method and the device simultaneously eliminate the influence of Gaussian noise and human hand physiological jitter on the motion of the slave robot through combining a Kalman filtering algorithm, a digital filter and a band-limited Fourier linear combination filter. The fitting effect is improved, and the occurrence of retina injury and potential safety hazards of the robot are avoided. Phase lag generated by the traditional low-pass filtering algorithm is effectively avoided.
FIG. 4 illustrates an internal block diagram of a computer device in one embodiment. The computer device may specifically be a terminal or a server. As shown in fig. 4, the computer device includes a processor, a memory, and a network interface connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and may also store a computer program that, when executed by a processor, causes the processor to implement an age identification method. The internal memory may also store a computer program which, when executed by the processor, causes the processor to perform the signal filtering method. Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
The present application also provides a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
The present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring an operation signal, the operation signal comprising: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
and filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (9)
1. A method of filtering a signal, the method comprising:
acquiring operation signals acquired by a main operator in subretinal injection operation, wherein the operation signals comprise: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
performing primary filtering processing on the Gaussian noise by a Kalman filtering algorithm;
carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through a digital filter;
filtering the human hand physiological jitter signals in the operation signals through a band-limited Fourier linear combination filter;
wherein, the first-stage filtering processing of the Gaussian noise by a Kalman filtering algorithm comprises the following steps: respectively establishing a motion model and an observation model;
the motion model:
（1）
the observation model:
（2）
the motion model is uniform motion, wherein,，/>the position and the speed of the moment k in the x, y and z directions; a is a transfer matrix, Q is a process noise variance matrix, H is an observation matrix, and R is an observation noise variance matrix; the expression is as follows:
（3）
（4）
（5）
（6）
wherein,
（7）
（8）
（9）
for the acquisition time interval, +.>、/>、/>For each direction continuous time variance +.>For each observed noise variance.
2. The method of claim 1, wherein the performing the second filtering process on the gaussian noise subjected to the first processing by the digital filter comprises:
the expression of the digital filter is as follows:
（10）
wherein,is the frequency response of the digital filter.
3. The method of claim 2, wherein filtering the human hand physiological dither signal in the operating signal by a band-limited fourier linear combination filter comprises:
determining an initialization coefficient vector, the initialization coefficient vector beingZero vector of the magnitude;
determining a window function, the window function beingLVector of size;
determining sine and cosine fitting values at the current moment;
determining an initial fitting error according to the initialization coefficient vector, the sine and cosine fitting value and an operation signal after Gaussian noise is filtered;
determining an initial in-band fitting signal according to the initialization coefficient vector, the window function and the sine and cosine fitting value;
determining an initial human hand movement track signal according to the initial in-band fitting signal and the operation signal after Gaussian noise is filtered;
and updating the initialization coefficient vector according to the initialization coefficient vector, the initial fitting error and the sine and cosine fitting numerical value.
4. The method of claim 3, wherein said determining an initial fitting error from said initialization coefficient vector, said sine and cosine fit values, and said gaussian noise filtered operating signal comprises:
（11）
wherein,error of initial fitting +.>To>Filtering the operation signal +.>Fitting a numerical value for sine and cosine of the current moment, +.>To initialize the coefficient vector.
5. A method according to claim 3, wherein said determining the initial hand movement trace signal from the initial in-band fit signal and the gaussian noise filtered out operation signal comprises:
（12）
wherein,for the human hand movement track signal, +.>To>Filtering the operation signal +.>The signal is fit for an initial in-band.
6. The method of claim 3, wherein the updating the initialization coefficient vector based on the initialization coefficient vector, an initial fitting error, and a sine-cosine fit value comprises:
（13）
wherein,for the updated initialization coefficient vector +.>For initializing coefficient vectors, ++>Convergence rate coefficient when being the minimum mean square error algorithm, < ->Error of initial fitting +.>And fitting a numerical value for the sine and cosine of the current moment.
7. A signal filtering apparatus, the apparatus comprising:
the acquisition module is used for acquiring operation signals, and the operation signals comprise: a human hand motion trail signal, a human hand physiological jitter signal and Gaussian noise;
the first filtering module is used for carrying out primary filtering processing on the Gaussian noise through a Kalman filtering algorithm; comprising the following steps: respectively establishing a motion model and an observation model;
the motion model:（1）
the observation model:
（2）
the motion model is uniform motion, wherein,，/>the position and the speed of the moment k in the x, y and z directions; a is a transfer matrix, Q is a process noise variance matrix, H is an observation matrix, and R is an observation noise variance matrix; the expression is as follows:
（3）
（4）
（5）
（6）
wherein,
（7）
（8）
（9）
for the acquisition time interval, +.>、/>、/>For each direction continuous time variance +.>For each observed noise variance; the second filtering module is used for carrying out secondary filtering treatment on the Gaussian noise subjected to primary treatment through the digital filter;
and the third filtering module is used for filtering the physiological shaking signals of the human hand in the operation signals through a band-limited Fourier linear combination filter.
8. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method of any of claims 1 to 6.
9. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the method of any one of claims 1 to 6.
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| CN202311411604.0A CN117137639B (en) | 2023-10-30 | 2023-10-30 | Signal filtering method, device, computer equipment and storage medium |
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| CN202311411604.0A CN117137639B (en) | 2023-10-30 | 2023-10-30 | Signal filtering method, device, computer equipment and storage medium |
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| KR20120114827A (en) * | 2011-04-08 | 2012-10-17 | 경북대학교 산학협력단 | Method and apparatus for tremor signal estimation for real time application and robot surgery system |
| CN114886572A (en) * | 2022-07-13 | 2022-08-12 | 杭州迪视医疗生物科技有限公司 | Main hand rocker in ophthalmic surgery |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| KR20120114827A (en) * | 2011-04-08 | 2012-10-17 | 경북대학교 산학협력단 | Method and apparatus for tremor signal estimation for real time application and robot surgery system |
| CN114886572A (en) * | 2022-07-13 | 2022-08-12 | 杭州迪视医疗生物科技有限公司 | Main hand rocker in ophthalmic surgery |
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| 主从式手术机器人主手消抖方法;姜紫阳;代煜;张建勋;孙会娇;;机器人;20181211(03);全文 * |
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